Deep learning based isocenter positioning and fully automated cardiac MR exam planning

ABSTRACT

A computer-implemented method of performing deep learning based isocenter positioning includes acquiring a plurality of slabs covering an anatomical area of interest that comprises a patient&#39;s heart. For each slab, one or more deep learning models are used to determine a likelihood score for the slab indicating a probability that the slab includes at least a portion of the patient&#39;s heart. A center position of the patient&#39;s heart may then be determined based on the likelihood scores determined for the plurality of slabs.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 62/371,281 filed Aug. 5, 2016, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to methods, systems, andapparatuses for deep learning based isocenter positioning and fullyautomated cardiac exam planning for Magnetic Resonance Imaging (MRI)applications.

BACKGROUND

Magnetic Resonance Imaging (MRI) of the heart typically involves theacquisition of standard views aligned with the heart axes.Time-efficient and reproducible planning requires expertise in heartgeometry and anatomy. One of the very first steps in cardiac MRI (CMRI)is to ensure that the heart is at the isocenter of the magnet. Thisrequires the technician to identify the heart location from a few (e.g.,three) localizer images obtained in standard orientations such ascoronal views. With automated isocenter positioning and view planning,CMRI view planning can be streamlined without manual interventions.However, heart location and appearance in the initial, limited number oflocalizer images vary greatly, depending on the scouting image planepositions and patient's anatomical characteristics. Automaticallylocalizing the heart from these scouts remains an open challenge and anintegrated fully automated CMRI planning is still being pursued. Inconventional clinical practice, isocenter positioning still relies ontechnicians to manually inspect the localizer images to identify theheart location and adjust the table position.

SUMMARY

Embodiments of the present invention address and overcome one or more ofthe above shortcomings and drawbacks, by providing methods, systems, andapparatuses for deep learning based isocenter positioning and fullyautomated cardiac exam planning for Magnetic Resonance Imaging (MRI)applications.

According to some embodiments of the present invention, acomputer-implemented method of performing deep learning based isocenterpositioning includes acquiring a plurality of slabs covering ananatomical area of interest that comprises a patient's heart. For eachslab, one or more deep learning models (e.g., convolutional neuralnetworks) are used to determine a likelihood score for the slabindicating a probability that the slab includes at least a portion ofthe patient's heart. A center position of the patient's heart may thenbe determined based on the likelihood scores determined for theplurality of slabs. In one embodiment, the method further includesdetermining a bounding box surrounding the patient's heart based on thelikelihood scores determined for the plurality of slabs.

In some embodiments of the aforementioned method, the slabs comprise afirst group of slabs acquired in a column direction with respect to theanatomical area of interest and a second group of slabs acquired in arow direction with respect to the anatomical area of interest. Thelikelihood score for each of the first group may be determined using afirst deep learning model trained using previously acquired slabsacquired in the column direction. Similarly, the likelihood score foreach of the second group may be determined using a second deep learningmodel trained using previously acquired slabs acquired in the rowdirection.

In some embodiments of the aforementioned method, the likelihood scorefor the slab comprises a plurality of likelihood data values. Eachlikelihood data value indicates a probability that a particular locationwithin the slab includes the patient's heart. In one embodiment, thecenter position associated with the patient's heart is determined byfirst identifying a cluster of values within the plurality of likelihooddata values and then determining a range of locations within the slabcorresponding to the cluster. The median location within the range oflocations is then designated as the center position of the heart. Priorto identifying the cluster of values, a predetermined threshold may beapplied to the likelihood data values to (a) replace likelihood datavalues above the predetermined threshold with a maximum value and (b)replace likelihood data values below the predetermined threshold arespecified as a minimum value.

Once the center position of the patient's heart is determined, it may beused in some embodiments for exam planning. For example, in oneembodiment, a region of interest is defined based on the center positionof the patient's heart. A stack of slices within the region of interestare acquired and used to reconstruct a 3D volume of the patient's heart.A left ventricle (LV) is segmented from the 3D MRI volume to yield asegmented LV. Then, a scan prescription for cardiac MRI acquisition canbe automatically generated based on cardiac anchor points provided bythe segmented LV in the 3D MRI volume.

According to another aspect of the present invention, as described insome embodiments, a system for performing deep learning based isocenterpositioning includes an MRI scanner and one or more computers. The MRIscanner is configured to acquire a plurality of 3D volumes covering ananatomical area of interest that comprises a patient's heart. These 3Dvolumes may include multiple groups of 3D volumes, with each group beingacquired in a different direction with respect to the anatomical area ofinterest. The computers are configured to perform an isocenterpositioning process which includes using one or more deep learningmodels to determine a likelihood score for each 3D volume indicating aprobability that the 3D volume includes at least a portion of thepatient's heart. The computer can then determine a center position ofthe patient's heart based on the likelihood scores determined for theplurality of 3D volumes. Techniques similar to those described abovewith respect to the method of performing deep learning based isocenterpositioning may be similarly applied to the aforementioned system.

According to other embodiments of the present invention, a method forperforming deep learning based isocenter positioning includes generatinga plurality of 3D volumes covering an anatomical area of interest thatcomprises a patient's heart based on a plurality of 2D scout images.Next, for each 3D volume, one or more deep learning models are used todetermine a likelihood score for the 3D volume indicating a probabilitythat the 3D volume includes at least a portion of the patient's heart.Then, a center position of the patient's heart is determined based onthe likelihood scores determined for the plurality of 3D volumes.

Additional features and advantages of the invention will be madeapparent from the following detailed description of illustrativeembodiments that proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects of the present invention are bestunderstood from the following detailed description when read inconnection with the accompanying drawings. For the purpose ofillustrating the invention, there are shown in the drawings embodimentsthat are presently preferred, it being understood, however, that theinvention is not limited to the specific instrumentalities disclosed.Included in the drawings are the following Figures:

FIG. 1 provides an illustration of deep learning based isocenterpositioning, as it may be applied in some embodiments;

FIG. 2 illustrates a method for deep learning based isocenterpositioning and fully automated cardiac MR exam planning, according tosome embodiments;

FIG. 3 shows example cases of automatic heart localization from coronalscouts, as may be acquired in some embodiments;

FIG. 4 shows an example MRI system which may be used to perform slabscanning and acquire stacks of slices, according to some embodiments;and

FIG. 5 provides an example of a parallel processing memory architecturethat may be utilized in some embodiments.

DETAILED DESCRIPTION

The following disclosure describes the present invention according toseveral embodiments directed at deep learning based isocenterpositioning and fully automated cardiac exam planning for MagneticResonance Imaging (MRI) applications. Briefly, a machine learning basedapproach is used to localize the heart from a few image scouts; then thelocalization information is used in a fully automated cardiac MRI (CMRI)planning method and system. In contrast to conventional machinelearning-based approaches that use pre-defined image features that arehand-crafted by an algorithm designer, the techniques presented hereinoffer a purely data-driven approach using deep learning. Additionally,instead of typical patch-based scanning in learning-based approaches,the techniques described herein use a directional slab scanning schemewhere image features are automatically learned to suit the task offinding the heart in scouts.

Conventional machine learning based approach may require pre-defined(hand-crafted) image features that the algorithm designer considers tobe relevant to the task, and which may not be accurate. Instead, recentdevelopments in Deep Learning (DL) shows that an end-to-end learningsystem can learn task-specific image features through the annotation oflarge representative datasets in a fully automated fashion. The essenceof DL is about learning multiple levels of representation andabstraction that help make sense of data such as images, sound, andtext. In particular, convolutional neural networks (CNNs), one of therepresentative deep learning architectures, have become powerful toolsin a broad range of computer vision tasks. CNNs are machine-learningmodels that represent mid-level and high-level abstractions obtainedfrom raw data (e.g., images). Various investigations indicate that thegeneric descriptors extracted from CNNs are effective in objectrecognition and localization in natural images. Recent development toleverage modern and customized GPUs makes DL based algorithms highlypractical.

In typical learning-based image processing workflows, a patch-basedscanning scheme is adopted. Each patch (i.e., sub-image) is evaluated byscanning through the entire image. Thus, for an image of size m by n,the number of patches/model-evaluations is in the order of O(m*n).

FIG. 1 provides an illustration of deep learning based isocenterpositioning, as it may be applied in some embodiments. The example shownin FIG. 1 is a slab-based scheme where each slab 105 (shown in dottedwhite) is a measurement volume that is fed into a deep learning network110 to calculate likelihood of containing the heart. In someembodiments, the output of the deep learning network is a likelihood vscolumn position curve. For example plot 115 shows the likelihood thatthe heart is present (shown along the y-axis) for a plurality of columnpositions (shown along the axis). For row slabs (not show in FIG. 1 ), asimilar plot may be generated for each row position. The totalslabs/model-evaluations are in the order of O(m+n). Once the likelihoodalong row/column is obtained at each row/column position, clusteranalysis is applied jointly along row and column to determine the heartlocation in the scout. A convolutional neural network architecture isshown in FIG. 1 for illustration purposes; however, in principle, othersimilar deep learning networks can be utilized. It should also be notedthat other acquisitions schemes may be utilized rather than therow/column technique described above. For example, although someaccuracy would be lost, a slab-based scheme which uses only row slabs oronly column slabs may be utilized with an appropriately-trained deeplearning network.

FIG. 2 illustrates a method 200 for deep learning based isocenterpositioning and fully automated cardiac MR exam planning, according tosome embodiments. This method 200 can be performed, for example, usingthe MRI system 400 and the parallel processing memory architecture 500shown in FIGS. 4 and 5 , respectively, and described in further detailbelow. An isocenter positioning workflow is performed at steps 205-215.Initially, at step 205, slab scanning is performed along row and columndirections. The term “slab” refers to a three-dimensional region ofanatomical area being interest. Thus, the anatomical area can beunderstood as being divided into a plurality of slabs. To acquire datain the row direction, vertical slabs of the anatomical area areacquired, and to acquire data in the column data horizontal slabs of theanatomical area are acquired. Combined, the horizontal and verticalslabs serve to provide a grid of data which can be used for heartlocalization, as described below. Notably, the acquisition of the slabsmay be done independently of one another and, in some embodiments, theacquisitions may be parallelized.

Next, at step 210, each slab acquired at step 205 is evaluated throughrow and column deep learning models to generate likelihood scores alongeach direction. These deep learning models (e.g., a CNN) are trainedbased on annotated images of slabs acquired from a large population ofpatients. In some embodiments, the annotation provided for each slab isa simple binary value indicating whether or not the heart is present. Inother embodiments, more detailed information may be provided such as theexact center position of the heart within the slab.

In some embodiments, a bounding box enclosing the heart may be providedin the annotation information. When the bounding box is present in theannotation data, it can be used for training. For example, if an exampleslab overlaps with the bounding box, the slab can be considered a“positive” example with respect to the presence of the heart.Conversely, if there is no overlap, then the slab may be considered a“negative” example. The positive and negative examples are then used totrain the deep learning models to generate a score for a new slabindicating the probability that the slab includes the heart (i.e., thenew slab intersects with the bounding box containing the heart.)

At step 215, clustering is applied to the likelihood scores generatedfor each slab to determine the heart location. To illustrate theclustering process, consider the plot of likelihood scores 115 shown inFIG. 1 . The “cluster” is between column positions 220 and 300 in thisexample. The heart position can then be determined by calculating thecenter point of the cluster (i.e., position 260). Note that, for theexample shown in FIG. 1 , thresholding was applied to ensure that eachscore was 0 and 1 (i.e., all scores above a predetermined threshold wereset to 1, while scores below the predetermined threshold are set to 0).However, thresholding is not necessarily required and, in otherembodiments, different techniques may be used for analyzing thelikelihood curves and finding their center. Additionally, in someembodiments, additional filtering may be applied to the output of themodels before clustering to ensure that a smooth curve is available foranalysis.

Note that the plot of likelihood scores 115 shown in FIG. 1 onlycontains a single cluster (i.e., between column positions 220 and 300).If the results of the deep learning model show multiple clusters, thenthe one with the most column positions may be selected as the clusterused for further processing. For example, the plot of likelihood scores115 included a cluster between column positions 100 and 150, along withthe cluster between column positions 220 and 300, the latter clusterwould be selected because it receives/presents larger total heartresponse than the former cluster.

Continuing with reference to FIG. 2 , at steps 220-235, a fullyautomated planning scheme is performed. It should be noted that theparticular technique shown in FIG. 2 is one example of an automatic viewplanning technique for cardiac magnetic resonance imaging acquisitionand, in other embodiments, other view planning techniques generallyknown in the art may be used in conjunction with the isocenterpositioning workflow described above.

Starting at step 220, a stack of slices on the canonical views(transverse or coronal) are acquired within a reduced region of interestthat is determined based on the heart localization results in isocenterpositioning. Next, at step 225, 3D model-based left ventricle (LV)segmentation is applied to a 3D volume reconstructed from the stack ofslices. At step 230, online slice prescription is performed based onsegmented LV for landmark detection. Then, at step 235, the landmarksare used to calculate standard cardiac view planes which are, in turn,provided to subsequent imaging steps for use as a basis for diagnosticscans.

Examples techniques for implementing steps 225-235 are described indetail in U.S. Pat. No. 8,948,484 to Lu et al., issued Feb. 3, 2015, andentitled “Method and system for automatic view planning for cardiacmagnetic resonance imaging acquisition” (“Lu”), the entirety of which isincorporated herein by reference. For example, in some embodiments ofthe present invention (and further explained in Lu), a LV is segmentedin the 3D MRI volume, and a scan prescription for cardiac MRIacquisition is automatically generated based on cardiac anchor pointsprovided by the segmented LV in the 3D MRI volume. A 3-chamber viewscanning plane can be determined based on the cardiac anchor pointsprovided by the segmented LV. Landmarks can be detected in amid-ventricular short axis slice reconstructed from the 3D MRI volumeand corresponding to a short axis slice prescribed in the short axisstack, and a 2-chamber view scanning plane and a 4-chamber view scanningplane can be determined based on the landmarks detected in thereconstructed mid-ventricular short axis slice together with thelandmark(s) inherent in the segmented LV such as apex.

Although isocenter positioning is described in FIGS. 1 and 2 withrespect to 2D scout images, it should be noted that the generaltechnique can be extended to 3D images as well. In embodiments where 3Dimages are used, the volumes used for positioning comprise a first groupof 3D volumes acquired in a first direction with respect to ananatomical area of interest and a second group of 3D volumes acquired ina second direction with respect to the anatomical area of interest.Then, the likelihood score used of locating the heart in each volume inthe first group is determined using a first deep learning model trainedusing previously acquired 3D volumes acquired in the first direction.Similarly, the likelihood score for each volume in the second group isdetermined using a second deep learning model trained using previouslyacquired 3D volumes acquired in the second direction. In someembodiments, rather than acquiring the 3D volumes directly, the 3Dvolumes are generated based on a plurality of 2D scout images.

Additionally, although the techniques described above with respect toFIGS. 1 and 2 focus on the left ventricle, these techniques are notlimited as such and may be extended to other portions of cardiacanatomy. For example, if models of the entire heart and great vesselsare available, automatic planning may be performed on the rightventricle, the atria, the great vessels—aorta, pulmonary artery, etc.

FIG. 3 shows example cases of automatic heart localization from coronalscouts. Each row presents three coronal scouts. Three numbers on top ofeach figure are [a:b c], where a for likelihood, b and c forautomatically detected heart row index and column index, respectively.The dotted line boxes indicate the scout with the highest localizationconfidence by the deep learning model for each case.

FIG. 4 shows an example MRI system 400 which may be used to perform slabscanning and acquire stacks of slices, according to some embodiments.This system 400 orders the acquisition of frequency domain componentsrepresenting MRI data for storage in a k-space storage array. In system400, magnetic coils 12 create a static base magnetic field in the bodyof patient 11 to be imaged and positioned on a table. Within the magnetsystem are gradient coils 14 for producing position dependent magneticfield gradients superimposed on the static magnetic field. Gradientcoils 14, in response to gradient signals supplied thereto by a gradientand shim coil control module 16, produce position dependent and shimmedmagnetic field gradients in three orthogonal directions and generatemagnetic field pulse sequences. The shimmed gradients compensate forinhomogeneity and variability in an MRI device magnetic field resultingfrom patient anatomical variation and other sources. The magnetic fieldgradients include a slice-selection gradient magnetic field, aphase-encoding gradient magnetic field and a readout gradient magneticfield that are applied to patient 11.

Further radio frequency (RF) module 20 provides RF pulse signals to RFcoil 18, which in response produces magnetic field pulses which rotatethe spins of the protons in the imaged body of the patient 11 by 90degrees or by 180 degrees for so-called “spin echo” imaging, or byangles less than or equal to 90 degrees for so-called “gradient echo”imaging. Gradient and shim coil control module 16 in conjunction with RFmodule 20, as directed by central control unit 26, controlslice-selection, phase-encoding, readout gradient magnetic fields, radiofrequency transmission, and magnetic resonance signal detection, toacquire magnetic resonance signals representing planar slices of patient11.

In response to applied RF pulse signals, the RF coil 18 receivesmagnetic resonance signals, i.e., signals from the excited protonswithin the body as they return to an equilibrium position established bythe static and gradient magnetic fields. The magnetic resonance signalsare detected and processed by a detector within RF module 20 and k-spacecomponent processor unit 34 to provide a magnetic resonance dataset toan image data processor for processing into an image. In someembodiments, the image data processor is located in central control unit26. However, in other embodiments such as the one depicted in FIG. 3 ,the image data processor is located in a separate unit 27.Electrocardiogram (ECG) synchronization signal generator 30 provides ECGsignals used for pulse sequence and imaging synchronization. A two orthree dimensional k-space storage array of individual data elements ink-space component processor unit 34 stores corresponding individualfrequency components comprising a magnetic resonance dataset. Thek-space array of individual data elements has a designated center andindividual data elements individually have a radius to the designatedcenter.

A magnetic field generator (comprising coils 12, 14, and 18) generates amagnetic field for use in acquiring multiple individual frequencycomponents corresponding to individual data elements in the storagearray. The individual frequency components are successively acquired inan order in which the radius of respective corresponding individual dataelements increases and decreases along a substantially spiral path asthe multiple individual frequency components are sequentially acquiredduring acquisition of a magnetic resonance dataset representing amagnetic resonance image. A storage processor in the k-space componentprocessor unit 34 stores individual frequency components acquired usingthe magnetic field in corresponding individual data elements in thearray. The radius of respective corresponding individual data elementsalternately increases and decreases as multiple sequential individualfrequency components are acquired. The magnetic field acquiresindividual frequency components in an order corresponding to a sequenceof substantially adjacent individual data elements in the array andmagnetic field gradient change between successively acquired frequencycomponents which is substantially minimized.

Central control unit 26 uses information stored in an internal databaseto process the detected magnetic resonance signals in a coordinatedmanner to generate high quality images of a selected slice(s) of thebody (e.g., using the image data processor) and adjusts other parametersof system 400. The stored information comprises predetermined pulsesequence and magnetic field gradient and strength data as well as dataindicating timing, orientation and spatial volume of gradient magneticfields to be applied in imaging. Generated images are presented ondisplay 40 of the operator interface. Computer 28 of the operatorinterface includes a graphical user interface (GUI) enabling userinteraction with central control unit 26 and enables user modificationof magnetic resonance imaging signals in substantially real time.Continuing with reference to FIG. 4 , display processor 37 processes themagnetic resonance signals to reconstruct one or more images forpresentation on display 40, for example. Various techniques may be usedfor reconstruction. For example, in conventional systems, anoptimization algorithm is applied to iteratively solve a cost functionwhich results in the reconstructed image.

FIG. 5 provides an example of a parallel processing memory architecture500 that may be utilized in some embodiments of the present invention.For example, this architecture 500 may be used for performingcalculations related to the deep learning networks discussed above withrespect to FIGS. 1 and 2 , as well as other general processing relatedto the techniques described herein. This architecture 500 may be used inembodiments of the present invention where NVIDIA™ CUDA (or a similarparallel computing platform) is used. The architecture includes a hostcomputing unit (“host”) 505 and a graphics processing unit (GPU) device(“device”) 510 connected via a bus 515 (e.g., a PCIe bus). The host 505includes the central processing unit, or “CPU” (not shown in FIG. 5 )and host memory 525 accessible to the CPU. The device 510 includes theGPU and its associated memory 520, referred to herein as device memory.The device memory 520 may include various types of memory, eachoptimized for different memory usages. For example, in some embodiments,the device memory includes global memory, constant memory, and texturememory.

Parallel portions of a deep learning application may be executed on thearchitecture 500 as “device kernels” or simply “kernels.” A kernelcomprises parameterized code configured to perform a particularfunction. The parallel computing platform is configured to execute thesekernels in an optimal manner across the architecture 500 based onparameters, settings, and other selections provided by the user.Additionally, in some embodiments, the parallel computing platform mayinclude additional functionality to allow for automatic processing ofkernels in an optimal manner with minimal input provided by the user.

The processing required for each kernel is performed by grid of threadblocks (described in greater detail below). Using concurrent kernelexecution, streams, and synchronization with lightweight events, thearchitecture 500 of FIG. 5 (or similar architectures) may be used toparallelize training of a deep neural network. For example, in someembodiments, a separate kernel is dedicated to determining a likelihoodscore for each column or row slab.

The device 510 includes one or more thread blocks 530 which representthe computation unit of the device 510. The term thread block refers toa group of threads that can cooperate via shared memory and synchronizetheir execution to coordinate memory accesses. For example, in FIG. 5 ,threads 540, 545 and 550 operate in thread block 530 and access sharedmemory 535. Depending on the parallel computing platform used, threadblocks may be organized in a grid structure. A computation or series ofcomputations may then be mapped onto this grid. For example, inembodiments utilizing CUDA, computations may be mapped on one-, two-, orthree-dimensional grids. Each grid contains multiple thread blocks, andeach thread block contains multiple threads. For example, in FIG. 5 ,the thread blocks 530 are organized in a two dimensional grid structurewith m+1 rows and n+1 columns. Generally, threads in different threadblocks of the same grid cannot communicate or synchronize with eachother. However, thread blocks in the same grid can run on the samemultiprocessor within the GPU at the same time. The number of threads ineach thread block may be limited by hardware or software constraints. Insome embodiments, the individual thread blocks can be selected andconfigured to optimize training of the deep learning network. Forexample, in one embodiment, each thread block is assigned a subset oftraining data with overlapping values. In other embodiments, threadblocks can be dedicated to different vertebral sections included in thespine.

Continuing with reference to FIG. 5 , registers 555, 560, and 565represent the fast memory available to thread block 530. Each registeris only accessible by a single thread. Thus, for example, register 555may only be accessed by thread 540. Conversely, shared memory isallocated per thread block, so all threads in the block have access tothe same shared memory. Thus, shared memory 535 is designed to beaccessed, in parallel, by each thread 540, 545, and 550 in thread block530. Threads can access data in shared memory 535 loaded from devicememory 520 by other threads within the same thread block (e.g., threadblock 530). The device memory 520 is accessed by all blocks of the gridand may be implemented using, for example, Dynamic Random-Access Memory(DRAM).

Each thread can have one or more levels of memory access. For example,in the architecture 500 of FIG. 5 , each thread may have three levels ofmemory access. First, each thread 540, 545, 550, can read and write toits corresponding registers 555, 560, and 565. Registers provide thefastest memory access to threads because there are no synchronizationissues and the register is generally located close to a multiprocessorexecuting the thread. Second, each thread 540, 545, 550 in thread block530, may read and write data to the shared memory 535 corresponding tothat block 530. Generally, the time required for a thread to accessshared memory exceeds that of register access due to the need tosynchronize access among all the threads in the thread block. However,like the registers in the thread block, the shared memory is typicallylocated close to the multiprocessor executing the threads. The thirdlevel of memory access allows all threads on the device 510 to readand/or write to the device memory. Device memory requires the longesttime to access because access must be synchronized across the threadblocks operating on the device. Thus, in some embodiments, theprocessing of each slab is coded such that it primarily utilizesregisters and shared memory and only utilizes device memory as necessaryto move data in and out of a thread block.

The embodiments of the present disclosure may be implemented with anycombination of hardware and software. For example, aside from parallelprocessing architecture presented in FIG. 5 , standard computingplatforms (e.g., servers, desktop computer, etc.) may be speciallyconfigured to perform the techniques discussed herein. In addition, theembodiments of the present disclosure may be included in an article ofmanufacture (e.g., one or more computer program products) having, forexample, computer-readable, non-transitory media. The media may haveembodied therein computer readable program code for providing andfacilitating the mechanisms of the embodiments of the presentdisclosure. The article of manufacture can be included as part of acomputer system or sold separately.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

An executable application, as used herein, comprises code or machinereadable instructions for conditioning the processor to implementpredetermined functions, such as those of an operating system, a contextdata acquisition system or other information processing system, forexample, in response to user command or input. An executable procedureis a segment of code or machine readable instruction, sub-routine, orother distinct section of code or portion of an executable applicationfor performing one or more particular processes. These processes mayinclude receiving input data and/or parameters, performing operations onreceived input data and/or performing functions in response to receivedinput parameters, and providing resulting output data and/or parameters.

The functions and process steps herein may be performed automatically orwholly or partially in response to user command. An activity (includinga step) performed automatically is performed in response to one or moreexecutable instructions or device operation without user directinitiation of the activity.

The system and processes of the figures are not exclusive. Othersystems, processes and menus may be derived in accordance with theprinciples of the invention to accomplish the same objectives. Althoughthis invention has been described with reference to particularembodiments, it is to be understood that the embodiments and variationsshown and described herein are for illustration purposes only.Modifications to the current design may be implemented by those skilledin the art, without departing from the scope of the invention. Asdescribed herein, the various systems, subsystems, agents, managers andprocesses can be implemented using hardware components, softwarecomponents, and/or combinations thereof. No claim element herein is tobe construed under the provisions of 35 U.S.C. 112, sixth paragraph,unless the element is expressly recited using the phrase “means for.”

The invention claimed is:
 1. A computer-implemented method of performingdeep learning based isocenter positioning, the method comprising:acquiring a plurality of slabs covering an anatomical area of interestthat comprises a patient's heart, including slabs oriented in a columndirection and in a row direction; for each slab in the column and rowdirection, using one or more deep learning models to determine aplurality of likelihood scores for the slab, each of the plurality oflikelihood scores indicating a probability that the slab includes atleast a portion of the patient's heart, wherein the one or more deeplearning models were trained based on annotated images of patients'anatomy to output the plurality of likelihood scores; determining areduced region of interest that is a subset of the anatomical area byfinding a center position of a cluster of the plurality of probabilityscores for the plurality of slabs that corresponds to the patient'sheart; acquiring a stack of slices in canonical directions in thereduced region of interest based on the determining step; andcalculating cardiac view planes for diagnostic imaging based onlandmarks within the stack of slices.
 2. The method of claim 1, whereinthe plurality of slabs comprise a first group of slabs acquired in acolumn direction with respect to the anatomical area of interest and asecond group of slabs acquired in a row direction with respect to theanatomical area of interest.
 3. The method of claim 2, wherein thelikelihood score for each of the first group of slabs is determinedusing a first deep learning model trained using previously acquiredslabs acquired in the column direction, and wherein the likelihood scorefor each of the second group of slabs is determined using a second deeplearning model trained using previously acquired slabs acquired in therow direction.
 4. The method of claim 1, wherein the likelihood scorefor the slab comprises a plurality of likelihood data values, eachlikelihood data value indicating a probability that a particularlocation within the slab includes the patient's heart.
 5. The method ofclaim 4, further comprising: identifying a cluster of values within theplurality of likelihood data values; identifying a range of locationswithin the slab corresponding to the cluster of values; identifying thecenter position associated with the patient's heart using a medianlocation within the range of locations.
 6. The method of claim 5,further comprising: prior to identifying the cluster of values, applyinga predetermined threshold to the plurality of likelihood data values to(a) replace likelihood data values above the predetermined thresholdwith a maximum value and (b) replace likelihood data values below thepredetermined threshold that are specified as a minimum value.
 7. Themethod of claim 1, further comprising: determining a bounding boxsurrounding the patient's heart based on the likelihood scoresdetermined for the plurality of slabs.
 8. The method of claim 1, whereinthe one or more deep learning models comprise a convolutional neuralnetwork.
 9. The method of claim 1, further comprising: reconstructing a3D Magnetic Resonance Imaging (MM) volume of the patient's heart basedon the stack of slices; segmenting a left ventricle (LV) in the 3D MRIvolume to yield a segmented LV; and automatically generating a scanprescription for cardiac MRI acquisition based on cardiac anchor pointsprovided by the segmented LV in the 3D MRI volume.
 10. A system forperforming deep learning based isocenter positioning, the systemcomprising: an MRI scanner configured to acquire a plurality of 3Dvolumes covering an anatomical area of interest that comprises apatient's heart, each 3D volume including slabs oriented in a columndirection and in a row direction; one or more computers configured toperform an isocenter positioning process comprising: for each 3D volume,using one or more deep learning models to determine a plurality oflikelihood scores for the 3D volume indicating a probability that the 3Dvolume includes at least a portion of the patient's heart, wherein theone or more deep learning models were trained based on annotated imagesof patients' anatomy to output the plurality of likelihood scores;determining a reduced region of interest that is a subset of theanatomical area by finding a center position of a cluster of theplurality of probability scores for the plurality of 3D volumes thatcorresponds to the patient's heart; acquiring a stack of slices incanonical directions in the reduced region of interest based on thedetermining step; and calculating cardiac view planes for diagnosticimaging based on landmarks within the stack of slices.
 11. The system ofclaim 10, wherein the plurality of 3D volumes comprise a first group of3D volumes acquired in a first direction with respect to the anatomicalarea of interest and a second group of 3D volumes acquired in a seconddirection with respect to the anatomical area of interest.
 12. Thesystem of claim 11, wherein the likelihood score for each of the firstgroup of 3D volumes is determined using a first deep learning modeltrained using previously acquired 3D volumes acquired in the firstdirection, and wherein the likelihood score for each of the second groupof 3D volumes is determined using a second deep learning model trainedusing previously acquired 3D volumes acquired in the second direction.13. The system of claim 10, wherein the likelihood score for the 3Dvolume comprises a plurality of likelihood data values, each likelihooddata value indicating a probability that a particular location withinthe 3D volume includes the patient's heart.
 14. The system of claim 13,wherein the isocenter positioning process further comprises: identifyinga cluster of values within the plurality of likelihood data values;identifying a range of locations within the 3D volume corresponding tothe cluster of values; identifying the center position associated withthe patient's heart using a median location within the range oflocations.
 15. The system of claim 14, wherein the isocenter positioningprocess further comprises: prior to identifying the cluster of values,applying a predetermined threshold to the plurality of likelihood datavalues to (a) replace likelihood data values above the predeterminedthreshold with a maximum value and (b) replace likelihood data valuesbelow the predetermined threshold are specified as a minimum value. 16.The system of claim 10, wherein the isocenter positioning processfurther comprises: determining a bounding box surrounding the patient'sheart based on the likelihood scores determined for the plurality of 3Dvolumes.
 17. The system of claim 10, wherein the one or more deeplearning models comprise a convolutional neural network.
 18. The systemof claim 10, wherein the one or more computers comprise a parallelcomputing platform which applies the one or more deep learning models tomultiple 3D volumes in parallel to determine likelihood score for the 3Dvolume indicating the probability that the 3D volume includes at least aportion of the patient's heart.
 19. The system of claim 10, wherein theisocenter positioning process further comprises: reconstructing a 3D MRIvolume of the patient's heart based on the stack of slices; segmenting aleft ventricle (LV) in the 3D MRI volume to yield a segmented LV; andautomatically generating a scan prescription for cardiac MRI acquisitionbased on cardiac anchor points provided by the segmented LV in the 3DMRI volume.
 20. A method for performing deep learning based isocenterpositioning, the method comprising: generating a plurality of 3D volumescovering an anatomical area of interest that comprises the patient'sheart based on a plurality of 2D scout images, each 3D volume includingslabs oriented in a column direction and in a row direction; for each 3Dvolume, using one or more deep learning models to determine a pluralityof likelihood scores for the 3D volume indicating a probability that the3D volume includes at least a portion of the patient's heart, whereinthe one or more deep learning models were trained based on annotatedimages of patients' anatomy to output the plurality of likelihoodscores; determining a reduced region of interest that is a subset of theanatomical area by finding a center position of a cluster of theplurality of probability scores for the plurality of 3D volumes thatcorresponds to the patient's heart; acquiring a stack of slices incanonical directions in the reduced region of interest based on thedetermining step; and calculating cardiac view planes for diagnosticimaging based on landmarks within the stack of slices.